# Machine-Learning (Zhou Zhihua)(周志华《机器学习》课后练习)
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My answers (i.e. ideas, code) for the book "Machine Learning (机器学习)" written by Prof. Zhou Zhihua.
ps. all the code exercises are implemented by **Python** in **eclipse-pydev** env.
for more info: welcome to my blog: [PnYuan - 周志华《机器学习》习题解答](https://pnyuan.github.io/blog/categories/%E5%91%A8%E5%BF%97%E5%8D%8E%E3%80%8A%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E3%80%8B%E4%B9%A0%E9%A2%98%E8%A7%A3%E7%AD%94/) and [CSDN - 周志华《机器学习》课后习题解答系列](http://blog.csdn.net/snoopy_yuan/article/details/62045353)
(更新中...2017-7-25)
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### [Ch6.Support Vector Machine (支持向量机)](./ch6_support_vector_machine/) ###
练习包括(exercises include):
- 支持向量分析实验([code here](./ch6_support_vector_machine/6.2_SVM_test/));
- 支持向量机与BP网络/C4.5决策树对比实验(experiment of SVM/BP/C4.5)([code here](./ch6_support_vector_machine/6.3_SVM_compare/));
- 线性核SVM与LDA的关系(comparison of linear kernel SVM and LDA);
- 高斯核SVM与RBF网络的关系(comparison of RBF kernel SVM and RBF net);
- SVM对噪声敏感的原因(reason of noise-sensitive);
- SVR实验(experiment of SVR)([code here](./ch6_support_vector_machine/6.8_SVR_test/));
- 核对率回归设计(about kernel logistic regression - KLR);
### [Ch5.Neural Networks (神经网络)](./ch5_neural_networks/) ###
练习包括(exercises include):
- 激活函数选择考虑(selection of activation function);
- Sigmoid激活函数与对率回归的联系(the relationships between Sigmoid() and Logistic Regression);
- BP算法推导(conduction of BP algorithm);
- 学习率分析(analysis of learning rate in NN training);
- 标准BP算法和累积BP算法对比实验(comparative experiment of BP)([code here](./ch5_neural_networks/5.5_BP/));
- BP算法改进(improvement of BP algorithm)([code here](./ch5_neural_networks/5.6_BP_improve/));
- RBF神经网络实现(implementation of RBF network)([code here](./ch5_neural_networks/5.7_RBF_BP/));
- SOM神经网络实验(experiment of SOM network)([code here](./ch5_neural_networks/5.8_SOM/));
- 卷积神经网络实验 - 字符识别(experiment of CNN on MNIST)([code here](./ch5_neural_networks/5.10_CNN/));
### [Ch4.Decision Tree (决策树)](./ch4_decision_tree/) ###
练习包括(exercises include):
- 决策树划分选择准则;
- 编程实现ID3算法(implementation of ID3)([code here](./ch4_decision_tree/4.3_ID3/));
- 编程实现CART算法与剪枝操作(implementation of CART and pruning)([code here](./ch4_decision_tree/4.4_CART/));
- 多变量决策树生成方式(multivariate decision tree);
- 非递归决策树生成方法(generation of decision - non-recursive approach using DFS/BFS)
### [Ch3.Linear Model (线性模型)](./ch3_linear_model/) ###
练习包括(exercises include):
- 分析偏置项b(bias terms);
- 证明对数似然是凸函数(convex functions);
- 编程实现对率回归(implementation of logistic regression)([code here](./ch3_linear_model/3.3_logistic_regression_watermelon/));
- 实验比较k-fold_CV和LOOCV(analysis of cross-validation)([code here](./ch3_linear_model/3.4_cross_validation/));
- 编程实现线性判别分析(implementation of LDA)([code here](./ch3_linear_model/3.5_LDA/));
- 线性判别分析的非线性拓展(nonlinear stretching of LDA);
- 最优ECOC编码方式;
- 多分类到二分类分解时类别不平衡的考虑(class-imbalance)。
### [Ch2.Model Evaluation and Selection (模型评估与选择)](./ch2_model_evaluation_and_selection/) ###
练习包括(exercises include):
- 分层抽样(stratified sampling)划分训练集与测试集;
- 留一法(leave-one-out)与k-折交叉验证法(k-fold cross validation)比较;
- F1值与BEP的关联;
- TPR、FPR、P、R之间的关联;
- AUC推导;
- 错误率与ROC的关系
- ROC曲线与代价曲线(cost-curve)的对应关系;
### [Ch1.Introduction (绪论)](./ch1_introduction/) ###
练习包括(exercises include):
- 求版本空间(version space);
- 析合范式(disjunctive normal form)提升假设空间(hypothesis space);
- 噪声环境(noise)下归纳偏好(inductive bias)考虑;
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